Data Cleaning

Load libraries

Code

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
source('dependencies.R')

Read in Data Sets

State Item Analysis Report

Code

HSPhy_2022_StateItemDF<-read_state_item("data/2022_Physics_District_NextGenMCASItem.xlsx", 2022, "PHY" )

HSPhy_2022_StateItemDF

Code

HSPhy_2023_StateItemDF<-read_state_item("data/2023_Physics_District_NextGenMCASItem.xlsx", 2023, "PHY" )

HSPhy_2023_StateItemDF

School Item Analysis Reports

Code

HSPhy_2022_SchoolItemDF<-read_school_item("data/2022_Physics_NextGenMCASItem.xlsx", 2022, "PHY")
HSPhy_2022_SchoolItemDF

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HSPhy_2022_SchoolItemDF<-HSPhy_2022_SchoolItemDF%>%
  left_join(HSPhy_2022_StateItemDF, by= c('Year'='Year', 'Subject'='Subject', 'ITEM' = 'ITEM'))%>%
  mutate(`School-State Diff` = `School%`- `State%`)
HSPhy_2022_SchoolItemDF

Code

HSPhy_2023_SchoolItemDF<-read_school_item("data/2023_Physics_NextGenMCASItem.xlsx", 2023, "PHY")
HSPhy_2023_SchoolItemDF

Code

HSPhy_2023_SchoolItemDF<-HSPhy_2023_SchoolItemDF%>%
  left_join(HSPhy_2023_StateItemDF, by= c('Year'='Year', 'Subject'='Subject', 'ITEM' = 'ITEM'))%>%
  mutate(`School-State Diff` = `School%`- `State%`)

HSPhy_2023_SchoolItemDF

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tail(HSPhy_2023_SchoolItemDF)

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tail(HSPhy_2022_SchoolItemDF)

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HSPhy_NextGen_SchoolItemDF <- rbind(HSPhy_2022_SchoolItemDF, HSPhy_2023_SchoolItemDF)

HSPhy_NextGen_SchoolItemDF

State NextGen Achievement Reports

Code

HSPhy_2022_StateAchievementDF<-read_state_achievement("data/2022_HSSci_NextGenMCAS.xlsx", 2022, "PHY" )
HSPhy_2022_StateAchievementDF

Code

HSPhy_2023_StateAchievementDF<-read_state_achievement("data/2023_HSSci_NextGenMCAS.xlsx", 2023, "PHY" )
HSPhy_2023_StateAchievementDF

Code

HSPhy_NextGen_StateAchievementDF<-rbind(HSPhy_2022_StateAchievementDF, HSPhy_2023_StateAchievementDF)
HSPhy_NextGen_StateAchievementDF

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length(unique(HSPhy_NextGen_StateAchievementDF$`School Code`))

[1] 112

Code

# HSPhy_NextGen_StateAchievementDF%>%
#   group_by(`School Code`, `Performance Level`)%>%
#   summarize(`Performance Count` = sum(`Performance Count`, na.rm = TRUE),
#             `Tested Students` = sum(`Tested Students`, na.rm = TRUE),
#             )

IT301 Reports

Code

SG9_standardXWalk<-read_excel("data/NextGenMCASItemxWalk.xlsx", sheet = "HS_Phys_StandardxWalk")

IT301_test<-read_excel("data/2023_Physics_IT301 MCAS District and School Test Item Analysis Summary.xlsx", skip = 14)
IT301_test

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IT301_2022<-read_IT301("data/2022_Physics_IT301 MCAS District and School Test Item Analysis Summary.xlsx", 2022, "PHY" )
IT301_2022

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IT301_2023<-read_IT301("data/2023_Physics_IT301 MCAS District and School Test Item Analysis Summary.xlsx", 2023, "PHY" )
IT301_2023

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NextGenIT301<- rbind(IT301_2022, IT301_2023)

HSPhy_NextGenIT301<-NextGenIT301%>%
  left_join(SG9_standardXWalk, by = "Standard")

HSPhy_NextGenIT301

Join IT301 Summary reports to SchoolItem performance Reports

Code

HSPhy_NextGen_SchoolIT301DF <- left_join(HSPhy_NextGen_SchoolItemDF, HSPhy_NextGenIT301, by = c("Year" = "Year", "Subject" = "Subject", "ITEM" = "ITEM"))

HSPhy_NextGen_SchoolIT301DF

Code

view(HSPhy_NextGen_SchoolIT301DF)
length(unique(HSPhy_NextGen_SchoolIT301DF$`School Name`))

[1] 112

Compute Key Summary Stats for IT301 Reports

Data of 112 schools performance on Next Generation HS Introductory Physics exam in Spring of 2022 and Spring of 2023. 87 of those schools tested students in both years and 25 of those schools only tested students in 1 of the 2 testing years.

Code

## Compute % Earned by Practice Category
## Compute State % Earned by Practice Category
## Compute SD by Practice Category
## Compute State % Earned by Reporting Category
## Compute SD by Reporting Category

Practice_Cat_Sum <- HSPhy_NextGen_SchoolIT301DF%>%
  group_by(`Practice Category`)%>%
  summarise(`PC Mean Diff` = mean(`School-State Diff`),
            `PC Med Diff` = median(`School-State Diff`),
            `PC SD Diff` = sd(`School-State Diff`))

Practice_Cat_Sum

Code

Practice_Cat_School_Sum <- HSPhy_NextGen_SchoolIT301DF%>%
  group_by(`School Name`,  `School Code`, `Practice Category`)%>%
  summarise(`PC Mean Diff` = mean(`School-State Diff`),
            `PC Med Diff` = median(`School-State Diff`),
            `PC SD Diff` = sd(`School-State Diff`))

Practice_Cat_School_Sum

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Reporting_Cat_Sum <- HSPhy_NextGen_SchoolIT301DF%>%
  group_by(`Reporting Category`)%>%
  summarise(`RC Mean Diff` = mean(`School-State Diff`),
            `RC Med Diff` = median(`School-State Diff`),
            `RC SD Diff` = sd(`School-State Diff`))

Reporting_Cat_Sum

Code

Reporting_Cat_School_Sum <- HSPhy_NextGen_SchoolIT301DF%>%
  group_by(`School Name`,  `School Code`, `Reporting Category`)%>%
  summarise(`RC Mean Diff` = mean(`School-State Diff`),
            `RC Med Diff` = median(`School-State Diff`),
            `RC SD Diff` = sd(`School-State Diff`))


Reporting_Cat_School_Sum

State E below average with Practice Category

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# State E below average
HSPhy_NextGen_StateAchievementDF

Code

LowEStateDF<- HSPhy_NextGen_StateAchievementDF%>%
  filter(`Performance Level` == "E")%>%
  filter(`Performance Diff` < 0)%>%
  mutate(`ELow` = TRUE)

LowEStateDF

Code

#view(LowEStateDF)

LowEPractice<-LowEStateDF%>%
  select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>%
  left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>%
  select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, 
         `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`)

LowEPractice%>%
  group_by(`Practice Category`)%>%
  summarise(`Low E PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE),
            `Low E PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE),
            `Low E PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE)
            )

State E above average with Practice Category

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HSPhy_NextGen_StateAchievementDF

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HighEStateDF<- HSPhy_NextGen_StateAchievementDF%>%
  filter(`Performance Level` == "E")%>%
  filter(`Performance Diff` > 0)%>%
  mutate(`ELow` = FALSE)

HighEStateDF

Code

#view(HighEStateDF)

HighEPractice<-HighEStateDF%>%
  select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>%
  left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>%
  select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, 
         `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`)

HighEPractice%>%
  group_by(`Practice Category`)%>%
  summarise(`High E PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE),
            `High E PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE),
            `High E PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE)
            )

State E and M above average

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HSPhy_NextGen_StateAchievementDF

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HighMEStateDF<- HSPhy_NextGen_StateAchievementDF%>%
  filter(`Performance Level` == "E" | `Performance Level` == "M")%>%
  filter(`Performance Diff` > 0)%>%
  mutate(`MELow` = FALSE)

HighMEStateDF

Code

#view(HighEStateDF)

HighMEPractice<-HighMEStateDF%>%
  select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>%
  left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>%
  select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, 
         `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`)

HighMEPractice%>%
  group_by(`Practice Category`)%>%
  summarise(`High ME PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE),
            `High ME PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE),
            `High ME PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE)
            )

State E or M below average

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HSPhy_NextGen_StateAchievementDF

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LowMEStateDF<- HSPhy_NextGen_StateAchievementDF%>%
  filter(`Performance Level` == "E" | `Performance Level` == "M")%>%
  filter(`Performance Diff` < 0)%>%
  mutate(`MELow` = FALSE)

LowMEStateDF

Code

#view(LowEStateDF)

LowMEPractice<-LowMEStateDF%>%
  select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>%
  left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>%
  select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, 
         `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`)

LowMEPractice%>%
  group_by(`Practice Category`)%>%
  summarise(`Low ME PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE),
            `Low ME PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE),
            `Low ME PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE)
            )

State E below, M above

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HSPhy_NextGen_StateAchievementDF

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LowEHighMStateDF<-HSPhy_NextGen_StateAchievementDF%>%
  mutate(`MHigh` = case_when(`Performance Level` == "M" & `Performance Diff` >= 0 ~ TRUE,
                             `Performance Level` == "M" & `Performance Diff` < 0 ~ FALSE))%>%
  mutate(`ELow` = case_when(`Performance Level` == "E" & `Performance Diff` < 0 ~ TRUE,
                            `Performance Level` == "E" & `Performance Diff` >= 0 ~ FALSE))%>%
  mutate(`EMOpp` = case_when(`MHigh` == TRUE && `ELow` == TRUE ~ TRUE))#%>%
  #filter(`EMOpp` == TRUE)#%>%
  #filter(`MHigh` == TRUE)
  

view(LowEHighMStateDF)

--- title: "Data Cleaning" format: html: df-print: paged toc: true code-fold: true code-copy: true code-tools: true editor: markdown: wrap: 72 --- # Load libraries ```{r} #| label: setup #| warning: false #| message: false knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) source('dependencies.R') ``` # Read in Data Sets ## State Item Analysis Report ```{r} HSPhy_2022_StateItemDF<-read_state_item("data/2022_Physics_District_NextGenMCASItem.xlsx", 2022, "PHY" ) HSPhy_2022_StateItemDF HSPhy_2023_StateItemDF<-read_state_item("data/2023_Physics_District_NextGenMCASItem.xlsx", 2023, "PHY" ) HSPhy_2023_StateItemDF ``` ## School Item Analysis Reports ```{r} HSPhy_2022_SchoolItemDF<-read_school_item("data/2022_Physics_NextGenMCASItem.xlsx", 2022, "PHY") HSPhy_2022_SchoolItemDF HSPhy_2022_SchoolItemDF<-HSPhy_2022_SchoolItemDF%>% left_join(HSPhy_2022_StateItemDF, by= c('Year'='Year', 'Subject'='Subject', 'ITEM' = 'ITEM'))%>% mutate(`School-State Diff` = `School%`- `State%`) HSPhy_2022_SchoolItemDF HSPhy_2023_SchoolItemDF<-read_school_item("data/2023_Physics_NextGenMCASItem.xlsx", 2023, "PHY") HSPhy_2023_SchoolItemDF HSPhy_2023_SchoolItemDF<-HSPhy_2023_SchoolItemDF%>% left_join(HSPhy_2023_StateItemDF, by= c('Year'='Year', 'Subject'='Subject', 'ITEM' = 'ITEM'))%>% mutate(`School-State Diff` = `School%`- `State%`) HSPhy_2023_SchoolItemDF tail(HSPhy_2023_SchoolItemDF) tail(HSPhy_2022_SchoolItemDF) HSPhy_NextGen_SchoolItemDF <- rbind(HSPhy_2022_SchoolItemDF, HSPhy_2023_SchoolItemDF) HSPhy_NextGen_SchoolItemDF ``` ## State NextGen Achievement Reports ```{r} HSPhy_2022_StateAchievementDF<-read_state_achievement("data/2022_HSSci_NextGenMCAS.xlsx", 2022, "PHY" ) HSPhy_2022_StateAchievementDF HSPhy_2023_StateAchievementDF<-read_state_achievement("data/2023_HSSci_NextGenMCAS.xlsx", 2023, "PHY" ) HSPhy_2023_StateAchievementDF HSPhy_NextGen_StateAchievementDF<-rbind(HSPhy_2022_StateAchievementDF, HSPhy_2023_StateAchievementDF) HSPhy_NextGen_StateAchievementDF length(unique(HSPhy_NextGen_StateAchievementDF$`School Code`)) # HSPhy_NextGen_StateAchievementDF%>% # group_by(`School Code`, `Performance Level`)%>% # summarize(`Performance Count` = sum(`Performance Count`, na.rm = TRUE), # `Tested Students` = sum(`Tested Students`, na.rm = TRUE), # ) ``` ## IT301 Reports ```{r} SG9_standardXWalk<-read_excel("data/NextGenMCASItemxWalk.xlsx", sheet = "HS_Phys_StandardxWalk") IT301_test<-read_excel("data/2023_Physics_IT301 MCAS District and School Test Item Analysis Summary.xlsx", skip = 14) IT301_test IT301_2022<-read_IT301("data/2022_Physics_IT301 MCAS District and School Test Item Analysis Summary.xlsx", 2022, "PHY" ) IT301_2022 IT301_2023<-read_IT301("data/2023_Physics_IT301 MCAS District and School Test Item Analysis Summary.xlsx", 2023, "PHY" ) IT301_2023 NextGenIT301<- rbind(IT301_2022, IT301_2023) HSPhy_NextGenIT301<-NextGenIT301%>% left_join(SG9_standardXWalk, by = "Standard") HSPhy_NextGenIT301 ``` ## Join IT301 Summary reports to SchoolItem performance Reports ```{r} HSPhy_NextGen_SchoolIT301DF <- left_join(HSPhy_NextGen_SchoolItemDF, HSPhy_NextGenIT301, by = c("Year" = "Year", "Subject" = "Subject", "ITEM" = "ITEM")) HSPhy_NextGen_SchoolIT301DF view(HSPhy_NextGen_SchoolIT301DF) length(unique(HSPhy_NextGen_SchoolIT301DF$`School Name`)) ``` ## Compute Key Summary Stats for IT301 Reports Data of 112 schools performance on Next Generation HS Introductory Physics exam in Spring of 2022 and Spring of 2023. 87 of those schools tested students in both years and 25 of those schools only tested students in 1 of the 2 testing years. ```{r} ## Compute % Earned by Practice Category ## Compute State % Earned by Practice Category ## Compute SD by Practice Category ## Compute State % Earned by Reporting Category ## Compute SD by Reporting Category Practice_Cat_Sum <- HSPhy_NextGen_SchoolIT301DF%>% group_by(`Practice Category`)%>% summarise(`PC Mean Diff` = mean(`School-State Diff`), `PC Med Diff` = median(`School-State Diff`), `PC SD Diff` = sd(`School-State Diff`)) Practice_Cat_Sum Practice_Cat_School_Sum <- HSPhy_NextGen_SchoolIT301DF%>% group_by(`School Name`, `School Code`, `Practice Category`)%>% summarise(`PC Mean Diff` = mean(`School-State Diff`), `PC Med Diff` = median(`School-State Diff`), `PC SD Diff` = sd(`School-State Diff`)) Practice_Cat_School_Sum Reporting_Cat_Sum <- HSPhy_NextGen_SchoolIT301DF%>% group_by(`Reporting Category`)%>% summarise(`RC Mean Diff` = mean(`School-State Diff`), `RC Med Diff` = median(`School-State Diff`), `RC SD Diff` = sd(`School-State Diff`)) Reporting_Cat_Sum Reporting_Cat_School_Sum <- HSPhy_NextGen_SchoolIT301DF%>% group_by(`School Name`, `School Code`, `Reporting Category`)%>% summarise(`RC Mean Diff` = mean(`School-State Diff`), `RC Med Diff` = median(`School-State Diff`), `RC SD Diff` = sd(`School-State Diff`)) Reporting_Cat_School_Sum ``` ## State E below average with Practice Category ```{r} # State E below average HSPhy_NextGen_StateAchievementDF LowEStateDF<- HSPhy_NextGen_StateAchievementDF%>% filter(`Performance Level` == "E")%>% filter(`Performance Diff` < 0)%>% mutate(`ELow` = TRUE) LowEStateDF #view(LowEStateDF) LowEPractice<-LowEStateDF%>% select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>% left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>% select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`) LowEPractice%>% group_by(`Practice Category`)%>% summarise(`Low E PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE), `Low E PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE), `Low E PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE) ) ``` ## State E above average with Practice Category ```{r} HSPhy_NextGen_StateAchievementDF HighEStateDF<- HSPhy_NextGen_StateAchievementDF%>% filter(`Performance Level` == "E")%>% filter(`Performance Diff` > 0)%>% mutate(`ELow` = FALSE) HighEStateDF #view(HighEStateDF) HighEPractice<-HighEStateDF%>% select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>% left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>% select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`) HighEPractice%>% group_by(`Practice Category`)%>% summarise(`High E PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE), `High E PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE), `High E PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE) ) ``` ## State E and M above average ```{r} HSPhy_NextGen_StateAchievementDF HighMEStateDF<- HSPhy_NextGen_StateAchievementDF%>% filter(`Performance Level` == "E" | `Performance Level` == "M")%>% filter(`Performance Diff` > 0)%>% mutate(`MELow` = FALSE) HighMEStateDF #view(HighEStateDF) HighMEPractice<-HighMEStateDF%>% select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>% left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>% select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`) HighMEPractice%>% group_by(`Practice Category`)%>% summarise(`High ME PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE), `High ME PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE), `High ME PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE) ) ``` ## State E or M below average ```{r} HSPhy_NextGen_StateAchievementDF LowMEStateDF<- HSPhy_NextGen_StateAchievementDF%>% filter(`Performance Level` == "E" | `Performance Level` == "M")%>% filter(`Performance Diff` < 0)%>% mutate(`MELow` = FALSE) LowMEStateDF #view(LowEStateDF) LowMEPractice<-LowMEStateDF%>% select(`Year`, `Subject`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`)%>% left_join(Practice_Cat_School_Sum, by = c( "School Code" = "School Code"))%>% select(`Year`, `School Name`, `School Code`, `Tested Students`, `Performance Level`, `Performance Count`, `Performance%`, `State Performance%`, `Performance Diff`, `Avg. Scaled Score`, `State Avg. Scaled Score`, `Practice Category`, `PC Mean Diff`, `PC Med Diff`, `PC SD Diff`) LowMEPractice%>% group_by(`Practice Category`)%>% summarise(`Low ME PC Mean Diff` = mean(`PC Mean Diff`, na.rm = TRUE), `Low ME PC Med Diff` = median(`PC Med Diff`, na.rm = TRUE), `Low ME PC SD Diff` = sd(`PC SD Diff`, na.rm = TRUE) ) ``` ## State E below, M above ```{r} HSPhy_NextGen_StateAchievementDF LowEHighMStateDF<-HSPhy_NextGen_StateAchievementDF%>% mutate(`MHigh` = case_when(`Performance Level` == "M" & `Performance Diff` >= 0 ~ TRUE, `Performance Level` == "M" & `Performance Diff` < 0 ~ FALSE))%>% mutate(`ELow` = case_when(`Performance Level` == "E" & `Performance Diff` < 0 ~ TRUE, `Performance Level` == "E" & `Performance Diff` >= 0 ~ FALSE))%>% mutate(`EMOpp` = case_when(`MHigh` == TRUE && `ELow` == TRUE ~ TRUE))#%>% #filter(`EMOpp` == TRUE)#%>% #filter(`MHigh` == TRUE) view(LowEHighMStateDF) ```